Grassmannian manifolds have been an effective way to represent image sets (video) which are mapped as data points on the manifold. Recognition can then be performed by applying the Discriminant Analysis (DA) on such manifolds. However, the local structure of the data points are not exploited in the DA. This paper proposes a modified Kernel Discriminant Analysis (KDA) approach on Grassmannian manifolds that utilizes the local structure of the data points on the manifold. The KDA exploits the local structure using between-class and within-class adjacency graphs that represent the between-class and within-class similarities, respectively. The maximum correlation from within-class and minimum correlation from between-class is utilized to define the connectivity between points in the graph thus exploiting the geometrical structure of the data. The discriminability is further improved by effective feature representation using LBP which can discriminate data across illumination, pose, and minor expressions. Effective recognition is performed by using only the cluster representatives extracted by clustering the frames of a video sequence. Experiments on several video datasets (Honda, MoBo, ChokePoint, NRC-IIT, and MOBIO) show that the proposed approach obtains better recognition rates, in comparison with the state-of-the-art approaches.
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